The long-term objective of this application is to establish the candidate as a leading member of multi-disciplinary research teams and as an independent researcher in statistical methodology for neuroimaging studies. The training plan complements the candidate?s strong statistical background by providing foundations in neuroscience, biomedical imaging, and mental health. The research plan focuses on new statistical methods for neuroimaging studies of mental health, in particular, schizophrenia, anxiety disorder, and addiction. One aim is to develop state-of-the-art statistical methodology to identify brain regions exhibiting similar functional magnetic resonance imaging (fMRI) profiles. Displays of colored brain images will distinguish clusters, and dynamic images will depict cluster changes across times, study conditions, or distance metrics. Several clustering methods will be compared and approximate approaches will be developed to increase computational efficiency. The clustering methodology will allow evaluation of the use of multiple brain regions by subjects when performing tasks, experiencing emotional states, or exhibiting certain behaviors. Other specific aims include developing statistical models for positron emission tomography (PET) and fMRI data incorporating intra-subject correlation. One method will use linear models with correlated errors and random effects. A second method will use Bayesian hierarchical models directly accounting for spatial correlation between and temporal correlation within brain voxels through various covariance models. Another approach will model temporal correlation directly and spatial correlation indirectly through prior distributions of voxel-specific parameters, e.g., pnors inducing similarity for neighboring voxels. Also, spatial networks will be extended to include voxels that are ?close? according to anatomical or physiological connections. Computer software for these research developments will be made accessible to neuroimaging scientists.